




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_ :						List[Any]				 =			{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_ :						List[Any]				 =			[
         """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaPreLayerNormForCausalLM""",
         """RobertaPreLayerNormForMaskedLM""",
         """RobertaPreLayerNormForMultipleChoice""",
         """RobertaPreLayerNormForQuestionAnswering""",
         """RobertaPreLayerNormForSequenceClassification""",
         """RobertaPreLayerNormForTokenClassification""",
         """RobertaPreLayerNormModel""",
         """RobertaPreLayerNormPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_ :						List[str]				 =			[
         """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaPreLayerNormForCausalLM""",
         """TFRobertaPreLayerNormForMaskedLM""",
         """TFRobertaPreLayerNormForMultipleChoice""",
         """TFRobertaPreLayerNormForQuestionAnswering""",
         """TFRobertaPreLayerNormForSequenceClassification""",
         """TFRobertaPreLayerNormForTokenClassification""",
         """TFRobertaPreLayerNormMainLayer""",
         """TFRobertaPreLayerNormModel""",
         """TFRobertaPreLayerNormPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_ :						Optional[Any]				 =			[
         """FlaxRobertaPreLayerNormForCausalLM""",
         """FlaxRobertaPreLayerNormForMaskedLM""",
         """FlaxRobertaPreLayerNormForMultipleChoice""",
         """FlaxRobertaPreLayerNormForQuestionAnswering""",
         """FlaxRobertaPreLayerNormForSequenceClassification""",
         """FlaxRobertaPreLayerNormForTokenClassification""",
         """FlaxRobertaPreLayerNormModel""",
         """FlaxRobertaPreLayerNormPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta_prelayernorm import (
         ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
         RobertaPreLayerNormConfig,
         RobertaPreLayerNormOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta_prelayernorm import (
              ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaPreLayerNormForCausalLM,
              RobertaPreLayerNormForMaskedLM,
              RobertaPreLayerNormForMultipleChoice,
              RobertaPreLayerNormForQuestionAnswering,
              RobertaPreLayerNormForSequenceClassification,
              RobertaPreLayerNormForTokenClassification,
              RobertaPreLayerNormModel,
              RobertaPreLayerNormPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta_prelayernorm import (
              TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaPreLayerNormForCausalLM,
              TFRobertaPreLayerNormForMaskedLM,
              TFRobertaPreLayerNormForMultipleChoice,
              TFRobertaPreLayerNormForQuestionAnswering,
              TFRobertaPreLayerNormForSequenceClassification,
              TFRobertaPreLayerNormForTokenClassification,
              TFRobertaPreLayerNormMainLayer,
              TFRobertaPreLayerNormModel,
              TFRobertaPreLayerNormPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta_prelayernorm import (
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormPreTrainedModel,
          )

else:
     import sys

     a_ :						Any				 =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










'''simple docstring'''







# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

lowerCAmelCase_     						=				'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
    raise ImportWarning(
        '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
    )

if version.parse(pyarrow.__version__).major < 8:
    raise ImportWarning(
        '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
        '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
    )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

lowerCAmelCase_     						=				concatenate_datasets
lowerCAmelCase_     						=				DownloadConfig
lowerCAmelCase_     						=				DownloadManager
lowerCAmelCase_     						=				DownloadMode
lowerCAmelCase_     						=				DownloadConfig
lowerCAmelCase_     						=				DownloadMode
lowerCAmelCase_     						=				DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


